This paper addresses measurement error in food security in the USA. In particular, it uses a Bayesian 4-parameter IRT model to look at the likelihood of over- or under-reporting of the conditions that comprise the food security module (FSM), the data collection administered in many US surveys to assess and monitor food insecurity. While this model’s parameters are only partially identified, we learn about the likely values of these parameters by using a Bayesian framework. My results suggest significant under-reporting of more severe food security items, particularly those in the child module. I find no evidence of over-reporting of food hardships. I show that, under conservative assumptions, this model predicts food insecurity prevalence between 1 and 3 percentage points higher than current estimates, or roughly 4 to 15 percent of prevalence, for the years 2007–2015. Results suggest much larger increases—on the order of 50 percent of prevalence—for very low food security among households that were screened into the food security module.

中文翻译：

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我们是否低估了粮食不安全？使用贝叶斯 4 参数 IRT 模型进行部分识别

本文讨论了美国食品安全的测量误差。特别是，它使用贝叶斯 4 参数 IRT 模型来查看构成食品安全模块 (FSM) 的条件报告过高或过低的可能性，该模块是在许多美国调查中管理的数据收集，用于评估和监测食品不安全感。虽然该模型的参数仅部分识别，但我们通过使用贝叶斯框架了解这些参数的可能值。我的结果表明，对更严重的食品安全项目，尤其是儿童模块中的项目，存在严重的漏报。我没有发现过度报告食物困难的证据。我表明，在保守假设下，该模型预测的粮食不安全流行率比当前估计值高 1 到 3 个百分点，或大约占流行率的 4% 到 15%，2007-2015 年。结果表明，在筛选到粮食安全模块的家庭中，粮食安全极低的家庭的增加幅度要大得多——大约是患病率的 50%。